All language subtitles for KU PMGT 823 Session 4 (Part B)- Quantitative

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:01,230 --> 00:00:06,650 Hello, everyone, and welcome to Part 2 of Session 4 in PMGT 823, Project Risk 2 00:00:06,650 --> 00:00:11,950 Management. In Part A, we focused on qualitative risk analysis, how to 3 00:00:11,950 --> 00:00:15,890 and prioritize risks based on expert judgment and experience. 4 00:00:16,329 --> 00:00:20,790 Now, in Part B, we will take things a step further with quantitative risk 5 00:00:20,790 --> 00:00:25,930 analysis. We will explore how to use numerical tools and models to estimate 6 00:00:25,930 --> 00:00:29,370 overall impact of risks on project outcomes. Let's get started. 7 00:00:32,270 --> 00:00:36,050 In this part of the module, we will focus on quantitative risk analysis. 8 00:00:36,730 --> 00:00:42,190 Unlike qualitative analysis, which is based on judgment and prioritization, 9 00:00:42,190 --> 00:00:46,030 approach uses numerical methods to measure overall risk exposure. 10 00:00:46,450 --> 00:00:51,470 We will look at techniques like expected monetary value, decision tree analysis, 11 00:00:51,650 --> 00:00:53,330 and Monte Carlo simulation. 12 00:00:53,850 --> 00:00:56,750 So if you are ready to dive into some numbers, let's go. 13 00:00:59,880 --> 00:01:04,220 Let's return to the full risk management process to see where quantitative risk 14 00:01:04,220 --> 00:01:05,280 analysis fits in. 15 00:01:05,900 --> 00:01:10,840 After planning for risk management, identifying risks, and performing a 16 00:01:10,840 --> 00:01:15,140 qualitative analysis, finally we arrive at step four that is highlighted here. 17 00:01:15,840 --> 00:01:20,800 This step uses numerical methods to estimate the combined effects of the 18 00:01:20,800 --> 00:01:22,620 on overall project objectives. 19 00:01:23,000 --> 00:01:29,280 It is especially helpful when decisions involve significant costs, timelines, or 20 00:01:29,280 --> 00:01:36,040 resource trade -offs similar to the previous part we have two 21 00:01:36,040 --> 00:01:40,900 important questions here what exactly is quantitative data and why is it 22 00:01:40,900 --> 00:01:46,340 important in risk analysis well quantitative data is information you can 23 00:01:46,340 --> 00:01:52,560 count or verify unlike qualitative data like i drink coffee every day 24 00:01:52,560 --> 00:01:56,600 quantitative data tells us exactly how much or how often 25 00:01:57,360 --> 00:02:03,500 For example, I drink four cups of coffee per day or consume 80 grams of coffee. 26 00:02:03,760 --> 00:02:06,940 Both of them are measurable numerical statements. 27 00:02:07,820 --> 00:02:12,780 In the context of project risk, we use this kind of data to analyze the 28 00:02:12,780 --> 00:02:17,960 impact of all risks on project objectives, such as total cost, 29 00:02:17,960 --> 00:02:19,900 variance, or potential delays. 30 00:02:20,520 --> 00:02:24,880 So one more time, what is the real benefit of using quantitative risk 31 00:02:25,720 --> 00:02:30,960 It gives us a clearer picture of our overall risk exposure and it helps us 32 00:02:30,960 --> 00:02:33,720 better informed decisions about the risk responses. 33 00:02:35,820 --> 00:02:38,840 Quantitative risk analysis has two main benefits. 34 00:02:39,320 --> 00:02:44,440 First, it is the only reliable method for assessing the overall level of 35 00:02:44,440 --> 00:02:50,200 risk because it evaluates the combined impact of all risks and uncertainties. 36 00:02:50,830 --> 00:02:55,870 Second, it gives us quantitative data that supports smarter decision -making. 37 00:02:56,290 --> 00:03:02,370 With this data, we can identify which risks deserve more attention and how we 38 00:03:02,370 --> 00:03:06,130 should respond to them, whether it is setting aside more budget, adjusting 39 00:03:06,130 --> 00:03:08,950 schedules, or preparing contingency plans. 40 00:03:09,690 --> 00:03:12,630 But to do this properly, we need a few things. 41 00:03:13,030 --> 00:03:15,730 First, we need high -quality risk data. 42 00:03:17,760 --> 00:03:22,420 we also need a clear baseline for scope, schedule, and cost. 43 00:03:22,720 --> 00:03:28,560 And of course, sometimes we even need specialized software or expert support 44 00:03:28,560 --> 00:03:30,100 build and interpret models. 45 00:03:32,880 --> 00:03:36,680 So when should you actually perform quantitative risk analysis? 46 00:03:37,300 --> 00:03:42,460 It is best suited for large or complex projects, projects with contractual 47 00:03:42,460 --> 00:03:46,240 requirements, or where stakeholders explicitly request it. 48 00:03:46,890 --> 00:03:51,150 You should also consider it when the project is strategically important for 49 00:03:51,150 --> 00:03:55,810 organization or if it is sensitive to delays and overruns. 50 00:03:56,070 --> 00:04:00,490 But even in these cases, you need to check a few things before jumping in. 51 00:04:01,570 --> 00:04:06,590 So say yes to quantitative analysis if you have the necessary tools and data. 52 00:04:06,970 --> 00:04:10,550 You're confident that most key risks have been identified. 53 00:04:11,450 --> 00:04:16,070 You have budget and time for the analysis, and there is low tolerance for 54 00:04:16,070 --> 00:04:17,370 or schedule deviations. 55 00:04:17,890 --> 00:04:23,470 On the other hand, if your data isn't accurate or subjective inputs work just 56 00:04:23,470 --> 00:04:28,090 well for you, then a qualitative approach might be more efficient and 57 00:04:28,090 --> 00:04:29,090 -effective. 58 00:04:31,170 --> 00:04:34,870 Here is the big picture of the quantitative risk analysis process. 59 00:04:35,410 --> 00:04:38,430 On the left, we start with a solid set of inputs. 60 00:04:38,780 --> 00:04:43,760 including the project management plan, risk register, and reliable cost and 61 00:04:43,760 --> 00:04:44,760 schedule estimates. 62 00:04:44,840 --> 00:04:48,480 Without these inputs, the analysis simply won't work. 63 00:04:48,800 --> 00:04:53,140 In the center, we see a range of tools and techniques used to perform the 64 00:04:53,140 --> 00:04:58,440 analysis. These include expert judgment and interviews, visual models like 65 00:04:58,440 --> 00:05:03,100 influence diagrams, and most importantly, simulation tools, 66 00:05:03,100 --> 00:05:05,280 analysis, and decision tree analysis. 67 00:05:06,120 --> 00:05:10,040 We will cover each of these in more detail in upcoming slides. 68 00:05:12,100 --> 00:05:16,400 Now let's look at the main tools and techniques used in quantitative risk 69 00:05:16,400 --> 00:05:21,620 analysis. We usually begin with the expert judgment, which plays a crucial 70 00:05:21,740 --> 00:05:27,000 especially when translating qualitative insights into numeric estimates or when 71 00:05:27,000 --> 00:05:28,540 interpreting complex results. 72 00:05:29,320 --> 00:05:34,000 Next, we have data gathering, often done through interviews with SMEs to get 73 00:05:34,000 --> 00:05:37,680 accurate data on risk probabilities, impact, and dependencies. 74 00:05:38,460 --> 00:05:43,540 Team skills are also vital because they help ensure the group state focus, 75 00:05:43,880 --> 00:05:48,500 minimize conflict, and support consensus building during risk workshops. 76 00:05:48,980 --> 00:05:51,760 Then we come to the representation of uncertainty. 77 00:05:52,780 --> 00:05:57,520 Here, we model uncertainties in things like cost or time using probability 78 00:05:57,520 --> 00:06:01,740 distributions such as triangular, normal, or beta distributions. 79 00:06:02,700 --> 00:06:08,700 And finally, we use data analysis techniques such as simulations to 80 00:06:08,700 --> 00:06:13,880 wide range of possible outcomes, sensitivity analysis to see which 81 00:06:13,880 --> 00:06:15,380 have the biggest impact, 82 00:06:16,170 --> 00:06:21,630 Decision tree analysis to compare different options under uncertainty and 83 00:06:21,630 --> 00:06:26,810 influence diagrams to show how variable and decisions interact visually. 84 00:06:29,610 --> 00:06:33,890 Let's now look at one of the most powerful tools in quantitative risk 85 00:06:33,890 --> 00:06:35,590 that is Monte Carlo simulation. 86 00:06:36,410 --> 00:06:41,570 This method allows us to evaluate how uncertainties in cost or time might 87 00:06:41,570 --> 00:06:42,830 the overall project outcomes. 88 00:06:43,450 --> 00:06:45,070 Here is how it works. 89 00:06:45,660 --> 00:06:50,180 We take input values like cost estimates or activity durations and let the 90 00:06:50,180 --> 00:06:53,220 computer randomly select values from their defined range. 91 00:06:53,500 --> 00:06:58,440 Then we run the model thousands of times and what we get as outputs are things 92 00:06:58,440 --> 00:07:03,920 like histograms showing the frequency of different results such as how often we 93 00:07:03,920 --> 00:07:05,180 finish under budget. 94 00:07:05,420 --> 00:07:10,320 And also S -curves, which gives us the cumulative probability of meeting a 95 00:07:10,320 --> 00:07:12,340 specific cost or a scheduled target. 96 00:07:12,910 --> 00:07:17,490 For example, if your S -curve shows that there is only a 60 % chance of 97 00:07:17,490 --> 00:07:21,790 completing project under $10 million, then it might be a red flag for you. 98 00:07:22,190 --> 00:07:27,070 And also, when we apply simulation to a scheduled risk, we can do a critical 99 00:07:27,070 --> 00:07:32,310 analysis to find out which activities are most likely to appear on the 100 00:07:32,310 --> 00:07:33,570 parts of the project. 101 00:08:16,110 --> 00:08:20,610 And here we have decision tree analysis that is a valuable tool for choosing the 102 00:08:20,610 --> 00:08:23,770 best option when faced multiple project passes. 103 00:08:24,250 --> 00:08:28,250 Each branch of the tree represents a decision or a chance event. 104 00:08:28,490 --> 00:08:33,049 For example, in this case, we are deciding whether to build a new plant or 105 00:08:33,049 --> 00:08:34,610 upgrade the existing one. 106 00:08:35,150 --> 00:08:39,990 The branches continue with possible future events like a strong or weak 107 00:08:40,590 --> 00:08:45,610 And each endpoint shows the resulting outcomes that can be either a gain or 108 00:08:45,610 --> 00:08:52,290 loss. Then to evaluate each branch, we use expected monetary value or EMV, 109 00:08:52,290 --> 00:08:55,910 is the weighted average of all possible outcomes along that path. 110 00:08:56,490 --> 00:09:01,270 For example, in this case, the decision to build a new plant leads to higher 111 00:09:01,270 --> 00:09:03,990 EMV. So that becomes the optimal choice. 112 00:09:06,440 --> 00:09:08,460 Now let's walk through this example. 113 00:09:08,780 --> 00:09:13,560 Imagine you are prime contractor of the project and the contract imposes a $1 114 00:09:13,560 --> 00:09:16,160 ,000 penalty per day of late delivery. 115 00:09:16,580 --> 00:09:18,900 Now you have two subcontractor options. 116 00:09:19,820 --> 00:09:22,860 Subcontractor A is the low -cost but risky option. 117 00:09:23,120 --> 00:09:28,220 They offer a cheaper bid, but there is a 50 % chance of a 90 -day delay, which 118 00:09:28,220 --> 00:09:30,700 could cost you $90 ,000 in penalties. 119 00:09:31,040 --> 00:09:36,070 On the other hand, Subcontractor B is more expensive but more reliable. 120 00:09:36,350 --> 00:09:41,770 They have only a 10 % chance of being 30 days late, meaning a smaller penalty 121 00:09:41,770 --> 00:09:43,030 that is $30 ,000. 122 00:09:43,430 --> 00:09:45,150 So how do you decide? 123 00:09:45,490 --> 00:09:50,970 You will use Expected Monetary Value or EMV. For each option, you combine the 124 00:09:50,970 --> 00:09:55,190 original bid with the probability -weighted penalty to get the expected 125 00:09:55,190 --> 00:09:57,050 cut. Let's see how it works. 126 00:09:58,250 --> 00:10:02,870 Now let's look at how we calculate the expected monetary value for each option. 127 00:10:03,210 --> 00:10:07,170 First, we start with subcontractor A that is low but risky choice. 128 00:10:07,990 --> 00:10:13,710 There is a 50 % chance of a $90 ,000 penalty so the total cost could go up to 129 00:10:13,710 --> 00:10:14,710 $200 ,000. 130 00:10:15,290 --> 00:10:22,250 But there is also a 50 % chance of no delay keeping the total cost at $110 131 00:10:23,050 --> 00:10:29,530 As a result, the EMV here that is the weighted average will be $155 ,000 for 132 00:10:29,530 --> 00:10:30,670 this subcontractor. 133 00:10:31,410 --> 00:10:36,150 Next, we evaluate the subcontractor B that is high but reliable one. 134 00:10:36,650 --> 00:10:43,090 With 10 % chance of a 30 -day delay, the total cost could raise up to $170 ,000. 135 00:10:43,610 --> 00:10:49,930 But in 90 % of the time, there is no delay and the cost stays at $140 ,000. 136 00:10:50,190 --> 00:10:55,130 So EMV will be $143 ,000 for this subcontractor. 137 00:10:55,920 --> 00:11:01,540 As a result, although subcontractor B has a higher bid, its lower risk results 138 00:11:01,540 --> 00:11:04,280 in more favorable expected monetary value. 139 00:11:04,540 --> 00:11:09,180 Therefore, subcontractor B is a smarter choice in terms of minimizing expected 140 00:11:09,180 --> 00:11:10,180 cost. 141 00:11:11,020 --> 00:11:15,980 Now let's talk about influence diagrams, which are powerful tools for analyzing 142 00:11:15,980 --> 00:11:17,740 decisions under uncertainty. 143 00:11:18,180 --> 00:11:22,840 They help us visualize relationships between three key elements that are 144 00:11:22,840 --> 00:11:28,570 decisions, like whether or not to invest on certain factors like R &D success 145 00:11:28,570 --> 00:11:32,170 and outcomes such as sales and net profit. 146 00:11:32,670 --> 00:11:38,410 These diagrams uses arrows to show how different elements influence each other. 147 00:11:38,950 --> 00:11:43,930 For example, here we see that our R &D investment decision affects the 148 00:11:43,930 --> 00:11:50,150 likelihood of R &D success, which in turn influence both sales and net 149 00:11:50,940 --> 00:11:56,280 These models often use probability distribution to represent uncertainty 150 00:11:56,280 --> 00:11:58,980 evaluated using simulation tools like Monte Carlo. 151 00:12:03,440 --> 00:12:09,040 FMEA, or failure moods and effects analysis, is a structured approach that 152 00:12:09,040 --> 00:12:14,160 us identify potential failures in a process, product, or system, and 153 00:12:14,160 --> 00:12:16,840 how these failures might affect overall performance. 154 00:12:17,580 --> 00:12:20,520 The key goal of FMEA is to prioritize risk. 155 00:12:20,760 --> 00:12:25,180 There is done using formula called risk priority number or RPN. 156 00:12:25,880 --> 00:12:31,660 RPN multiplies three factors that are severity or how serious the impact of 157 00:12:31,660 --> 00:12:37,460 would be, occurrence or how likely it is to happen, and detection or how likely 158 00:12:37,460 --> 00:12:39,700 we are to notice the risk before it happens. 159 00:12:40,510 --> 00:12:45,790 Each of these factors is scored on a scale from 1 to 10 and by multiplying 160 00:12:45,790 --> 00:12:49,330 scores together we get an RPN between 1 and 1000. 161 00:12:49,930 --> 00:12:54,950 Higher RPN values mean more critical risks which should be addressed first. 162 00:12:56,570 --> 00:13:01,510 Here we apply the FMEA technique to a real world ID project that is the 163 00:13:01,510 --> 00:13:03,550 implementation of a new CRM system. 164 00:13:04,150 --> 00:13:09,030 Imagine during the risk identification phase the team highlighted three major 165 00:13:09,030 --> 00:13:14,480 risks. that are inadequate training for end users, delays in server delivery by 166 00:13:14,480 --> 00:13:18,200 the supplier, and data transfer errors from the legacy system. 167 00:13:18,420 --> 00:13:23,640 For each risk, we assessed the severity, occurrence, and detectability on a 168 00:13:23,640 --> 00:13:27,300 scale from 1 to 10 and then calculated the risk priority number. 169 00:13:27,700 --> 00:13:33,740 The results show that data transfer errors had the highest RPN of 315. 170 00:13:34,570 --> 00:13:38,870 This means it is the most critical risk and should be addressed first in our 171 00:13:38,870 --> 00:13:39,870 mitigation plans. 172 00:13:39,910 --> 00:13:45,850 The other two risks that are inadequate training with an RPN of 210 and server 173 00:13:45,850 --> 00:13:50,690 delay with an RPN of 96 are still important but with lower priority. 174 00:13:53,130 --> 00:13:57,410 One of the most important outcomes of quantitative risk analysis is the risk 175 00:13:57,410 --> 00:14:02,270 report. This report gives us a clear data -driven picture of the project's 176 00:14:02,270 --> 00:14:06,630 overall exposure to risk using both numerical results and narrative 177 00:14:07,070 --> 00:14:11,890 It also helps us update the key project documents including the overall risk 178 00:14:11,890 --> 00:14:17,690 exposure, detailed probabilistic analysis, and a prioritized list of 179 00:14:17,690 --> 00:14:22,840 risks. In addition, we can track trends across analysis results and determine 180 00:14:22,840 --> 00:14:25,140 which risk response are most appropriate. 181 00:14:27,760 --> 00:14:31,800 Before our next session, please make sure to complete the following readings. 182 00:14:32,100 --> 00:14:38,600 Start with chapters 7 and 9 from Kendrick and also review sections 11 .3 183 00:14:38,600 --> 00:14:41,320 .4 from the PEMBOX 6th edition. 184 00:14:43,690 --> 00:14:47,130 To reinforce your learning, please complete the following activities. 185 00:14:47,390 --> 00:14:51,690 First, reply to at least two of your classmates' posts on Discussion Board 2. 186 00:14:51,890 --> 00:14:57,350 Then take Quiz 3, complete Problem 1, and submit Project Milestone 3. 187 00:14:58,050 --> 00:15:01,310 Finally, please take a few minutes to review the Case Study 4. 188 00:15:03,150 --> 00:15:07,550 And that brings us to the end of Part B of our session on Quantitative Risk 189 00:15:07,550 --> 00:15:11,970 Analysis. If you have any questions, please don't hesitate to reach out. You 190 00:15:11,970 --> 00:15:12,970 also email me. 191 00:15:13,180 --> 00:15:15,060 Thank you very much for watching this video 18392

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